A systematic review of three approaches for constructing physical activity messages: What messages work and what improvements are needed?
Bibliographic record
Abstract
BACKGROUND: To motivate individuals to adhere to a regular physical activity regime, guidelines must be supplemented with persuasive messages that are disseminated widely. While substantial research has examined effective strategies for disseminating physical activity messages, there has been no systematic effort to examine optimal message content. This paper reviews studies that evaluate the effectiveness of three approaches for constructing physical activity messages including tailoring messages to suit individual characteristics of message recipients (message tailoring), framing messages in terms of gains versus losses (message framing), and targeting messages to affect change in self-efficacy (i.e., a theoretical determinant of behavior change). METHODS: We searched the MEDLINE, PsycINFO, EMBASE and CINAHL databases up to July 2008. Relevant reference lists also were searched. We included intervention trials, field experiments, and laboratory-based studies that aimed to test the efficacy or effectiveness of tailored messages, framed messages and self-efficacy change messages among healthy adults. We used a descriptive approach to analyze emerging patterns in research findings. Based on this evidence we made recommendations for practice and future research. RESULTS: Twenty-two studies were identified. Twelve studies evaluated message tailoring. In 10 of these studies, tailored messages resulted in greater physical activity than a control message. Six studies evaluated framed messages. Five of these studies demonstrated that gain-framed messages lead to stronger intentions to be active compared to a control message. Moreover, a gain-frame advantage was evident in three of the four studies that assessed physical activity. Four studies evaluated self-efficacy change messages. The two studies that used an experimental design provide a clear indication that individuals' beliefs can be affected by messages that incorporate types of information known to be determinants of self-efficacy. Overall, strong evidence to support definitive recommendations for optimal message content and structure was lacking. CONCLUSIONS: Additional research testing the optimal content of messages used to supplement physical activity guidelines is needed. Tailored messages, gain-framed messages, and self-efficacy change messages hold promise as strategies for constructing physical activity messages and should be a focus of future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".